modin.pandas.DataFrame.drop_duplicates¶
- DataFrame.drop_duplicates(subset=None, keep='first', inplace=False, ignore_index=False)[source]¶
Return
DataFrame
with duplicate rows removed.Considering certain columns is optional. Indexes, including time indexes are ignored.
- Parameters:
subset (column label or sequence of labels, optional) – Only consider certain columns for identifying duplicates, by default use all columns.
keep ({'first', 'last', False}, default 'first') – Determines which duplicates (if any) to keep. ‘first’ : Drop duplicates except for the first occurrence. ‘last’ : Drop duplicates except for the last occurrence. False : Drop all duplicates.
inplace (bool, default False) – Whether to modify the DataFrame rather than creating a new one.
ignore_index (bool, default False) – If True, the resulting axis will be labeled 0, 1, …, n - 1.
- Returns:
DataFrame with duplicates removed or None if inplace=True.
- Return type:
DataFrame or None
Examples
Consider dataset containing ramen rating.
>>> df = pd.DataFrame({ ... 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'], ... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'], ... 'rating': [4, 4, 3.5, 15, 5] ... }) >>> df brand style rating 0 Yum Yum cup 4.0 1 Yum Yum cup 4.0 2 Indomie cup 3.5 3 Indomie pack 15.0 4 Indomie pack 5.0
By default, it removes duplicate rows based on all columns.
>>> df.drop_duplicates() brand style rating 0 Yum Yum cup 4.0 2 Indomie cup 3.5 3 Indomie pack 15.0 4 Indomie pack 5.0
To remove duplicates on specific column(s), use subset.
>>> df.drop_duplicates(subset=['brand']) brand style rating 0 Yum Yum cup 4.0 2 Indomie cup 3.5
To remove duplicates and keep last occurrences, use keep.
>>> df.drop_duplicates(subset=['brand', 'style'], keep='last') brand style rating 1 Yum Yum cup 4.0 2 Indomie cup 3.5 4 Indomie pack 5.0